{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Copyright 2019 NVIDIA Corporation. All Rights Reserved.\n",
    "#\n",
    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "#     http://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License.\n",
    "# =============================================================================="
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png\" style=\"width: 90px; float: right;\">\n",
    "\n",
    "# Torch-TensorRT Getting Started - ResNet 50"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview\n",
    "\n",
    "In the practice of developing machine learning models, there are few tools as approachable as PyTorch for developing and experimenting in designing machine learning models. The power of PyTorch comes from its deep integration into Python, its flexibility and its approach to automatic differentiation and execution (eager execution). However, when moving from research into production, the requirements change and we may no longer want that deep Python integration and we want optimization to get the best performance we can on our deployment platform. In PyTorch 1.0, TorchScript was introduced as a method to separate your PyTorch model from Python, make it portable and optimizable. TorchScript uses PyTorch's JIT compiler to transform your normal PyTorch code which gets interpreted by the Python interpreter to an intermediate representation (IR) which can have optimizations run on it and at runtime can get interpreted by the PyTorch JIT interpreter. For PyTorch this has opened up a whole new world of possibilities, including deployment in other languages like C++. It also introduces a structured graph based format that we can use to do down to the kernel level optimization of models for inference.\n",
    "\n",
    "When deploying on NVIDIA GPUs TensorRT, NVIDIA's Deep Learning Optimization SDK and Runtime is able to take models from any major framework and specifically tune them to perform better on specific target hardware in the NVIDIA family be it an A100, TITAN V, Jetson Xavier or NVIDIA's Deep Learning Accelerator. TensorRT performs a couple sets of optimizations to achieve this. TensorRT fuses layers and tensors in the model graph, it then uses a large kernel library to select implementations that perform best on the target GPU. TensorRT also has strong support for reduced operating precision execution which allows users to leverage the Tensor Cores on Volta and newer GPUs as well as reducing memory and computation footprints on device.\n",
    "\n",
    "Torch-TensorRT is a compiler that uses TensorRT to optimize TorchScript code, compiling standard TorchScript modules into ones that internally run with TensorRT optimizations. This enables you to continue to remain in the PyTorch ecosystem, using all the great features PyTorch has such as module composability, its flexible tensor implementation, data loaders and more. Torch-TensorRT is available to use with both PyTorch and LibTorch."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Learning objectives\n",
    "\n",
    "This notebook demonstrates the steps for compiling a TorchScript module with Torch-TensorRT on a pretrained ResNet-50 network, and running it to test the speedup obtained.\n",
    "\n",
    "## Content\n",
    "1. [Requirements](#1)\n",
    "1. [ResNet-50 Overview](#2)\n",
    "1. [Creating TorchScript modules](#3)\n",
    "1. [Compiling with Torch-TensorRT](#4)\n",
    "1. [Conclusion](#5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: numpy==1.21.2 in /opt/conda/lib/python3.8/site-packages (1.21.2)\n",
      "Requirement already satisfied: scipy==1.5.2 in /opt/conda/lib/python3.8/site-packages (1.5.2)\n",
      "Requirement already satisfied: Pillow==6.2.0 in /opt/conda/lib/python3.8/site-packages (6.2.0)\n",
      "Requirement already satisfied: scikit-image==0.17.2 in /opt/conda/lib/python3.8/site-packages (0.17.2)\n",
      "Requirement already satisfied: matplotlib==3.3.0 in /opt/conda/lib/python3.8/site-packages (3.3.0)\n",
      "Requirement already satisfied: PyWavelets>=1.1.1 in /opt/conda/lib/python3.8/site-packages (from scikit-image==0.17.2) (1.1.1)\n",
      "Requirement already satisfied: tifffile>=2019.7.26 in /opt/conda/lib/python3.8/site-packages (from scikit-image==0.17.2) (2021.10.12)\n",
      "Requirement already satisfied: networkx>=2.0 in /opt/conda/lib/python3.8/site-packages (from scikit-image==0.17.2) (2.0)\n",
      "Requirement already satisfied: imageio>=2.3.0 in /opt/conda/lib/python3.8/site-packages (from scikit-image==0.17.2) (2.9.0)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in /opt/conda/lib/python3.8/site-packages (from matplotlib==3.3.0) (2.4.7)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /opt/conda/lib/python3.8/site-packages (from matplotlib==3.3.0) (2.8.2)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.8/site-packages (from matplotlib==3.3.0) (1.3.2)\n",
      "Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.8/site-packages (from matplotlib==3.3.0) (0.10.0)\n",
      "Requirement already satisfied: six in /opt/conda/lib/python3.8/site-packages (from cycler>=0.10->matplotlib==3.3.0) (1.16.0)\n",
      "Requirement already satisfied: decorator>=4.1.0 in /opt/conda/lib/python3.8/site-packages (from networkx>=2.0->scikit-image==0.17.2) (5.0.9)\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n",
      "Tue Oct 26 00:13:31 2021       \n",
      "+-----------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 450.51.06    Driver Version: 450.51.06    CUDA Version: 11.4     |\n",
      "|-------------------------------+----------------------+----------------------+\n",
      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
      "|                               |                      |               MIG M. |\n",
      "|===============================+======================+======================|\n",
      "|   0  Tesla V100-PCIE...  On   | 00000000:1A:00.0 Off |                    0 |\n",
      "| N/A   37C    P0    24W / 250W |      0MiB / 32510MiB |      0%      Default |\n",
      "|                               |                      |                  N/A |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "|   1  Tesla V100-PCIE...  On   | 00000000:1B:00.0 Off |                    0 |\n",
      "| N/A   33C    P0    22W / 250W |      0MiB / 32510MiB |      0%      Default |\n",
      "|                               |                      |                  N/A |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "|   2  Tesla V100-PCIE...  On   | 00000000:3D:00.0 Off |                    0 |\n",
      "| N/A   32C    P0    24W / 250W |      0MiB / 32510MiB |      0%      Default |\n",
      "|                               |                      |                  N/A |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "|   3  Tesla V100-PCIE...  On   | 00000000:3E:00.0 Off |                    0 |\n",
      "| N/A   33C    P0    24W / 250W |      0MiB / 32510MiB |      0%      Default |\n",
      "|                               |                      |                  N/A |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "|   4  Tesla V100-PCIE...  On   | 00000000:88:00.0 Off |                    0 |\n",
      "| N/A   32C    P0    25W / 250W |      0MiB / 32510MiB |      0%      Default |\n",
      "|                               |                      |                  N/A |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "|   5  Tesla V100-PCIE...  On   | 00000000:89:00.0 Off |                    0 |\n",
      "| N/A   31C    P0    22W / 250W |      0MiB / 32510MiB |      0%      Default |\n",
      "|                               |                      |                  N/A |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "|   6  Tesla V100-PCIE...  On   | 00000000:B1:00.0 Off |                    0 |\n",
      "| N/A   32C    P0    24W / 250W |      0MiB / 32510MiB |      0%      Default |\n",
      "|                               |                      |                  N/A |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "|   7  Tesla V100-PCIE...  On   | 00000000:B2:00.0 Off |                    0 |\n",
      "| N/A   32C    P0    25W / 250W |      0MiB / 32510MiB |      0%      Default |\n",
      "|                               |                      |                  N/A |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "                                                                               \n",
      "+-----------------------------------------------------------------------------+\n",
      "| Processes:                                                                  |\n",
      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
      "|        ID   ID                                                   Usage      |\n",
      "|=============================================================================|\n",
      "|  No running processes found                                                 |\n",
      "+-----------------------------------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "!pip install numpy==1.21.2 scipy==1.5.2 Pillow==6.2.0 scikit-image==0.17.2 matplotlib==3.3.0\n",
    "!nvidia-smi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"1\"></a>\n",
    "## 1. Requirements\n",
    "\n",
    "Follow the steps in `notebooks/README` to prepare a Docker container, within which you can run this notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"2\"></a>\n",
    "## 2. ResNet-50 Overview\n",
    "\n",
    "\n",
    "PyTorch has a model repository called the PyTorch Hub, which is a source for high quality implementations of common models. We can get our ResNet-50 model from there pretrained on ImageNet.\n",
    "\n",
    "### Model Description\n",
    "\n",
    "This ResNet-50 model is based on the [Deep Residual Learning for Image Recognition](https://arxiv.org/pdf/1512.03385.pdf) paper, which describes ResNet as “a method for detecting objects in images using a single deep neural network\". The input size is fixed to 32x32.\n",
    "\n",
    "<img src=\"https://pytorch.org/assets/images/resnet.png\" alt=\"alt\" width=\"50%\"/>\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in /root/.cache/torch/hub/pytorch_vision_v0.10.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ResNet(\n",
       "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (relu): ReLU(inplace=True)\n",
       "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "  (layer1): Sequential(\n",
       "    (0): Bottleneck(\n",
       "      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Bottleneck(\n",
       "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (layer2): Sequential(\n",
       "    (0): Bottleneck(\n",
       "      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Bottleneck(\n",
       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (3): Bottleneck(\n",
       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (layer3): Sequential(\n",
       "    (0): Bottleneck(\n",
       "      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (3): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (4): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (5): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (layer4): Sequential(\n",
       "    (0): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Bottleneck(\n",
       "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
       "  (fc): Linear(in_features=2048, out_features=1000, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "\n",
    "torch.hub._validate_not_a_forked_repo=lambda a,b,c: True\n",
    "\n",
    "resnet50_model = torch.hub.load('pytorch/vision:v0.10.0', 'resnet50', pretrained=True)\n",
    "resnet50_model.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All pre-trained models expect input images normalized in the same way,\n",
    "i.e. mini-batches of 3-channel RGB images of shape `(3 x H x W)`, where `H` and `W` are expected to be at least `224`.\n",
    "The images have to be loaded in to a range of `[0, 1]` and then normalized using `mean = [0.485, 0.456, 0.406]`\n",
    "and `std = [0.229, 0.224, 0.225]`.\n",
    "\n",
    "Here's a sample execution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mkdir: cannot create directory ‘./data’: File exists\n",
      "--2021-10-26 00:13:33--  https://d17fnq9dkz9hgj.cloudfront.net/breed-uploads/2018/08/siberian-husky-detail.jpg?bust=1535566590&width=630\n",
      "Resolving d17fnq9dkz9hgj.cloudfront.net (d17fnq9dkz9hgj.cloudfront.net)... 13.226.251.36, 13.226.251.27, 13.226.251.107, ...\n",
      "Connecting to d17fnq9dkz9hgj.cloudfront.net (d17fnq9dkz9hgj.cloudfront.net)|13.226.251.36|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 24112 (24K) [image/jpeg]\n",
      "Saving to: ‘./data/img0.JPG’\n",
      "\n",
      "./data/img0.JPG     100%[===================>]  23.55K  --.-KB/s    in 0.002s  \n",
      "\n",
      "2021-10-26 00:13:34 (13.1 MB/s) - ‘./data/img0.JPG’ saved [24112/24112]\n",
      "\n",
      "--2021-10-26 00:13:34--  https://www.hakaimagazine.com/wp-content/uploads/header-gulf-birds.jpg\n",
      "Resolving www.hakaimagazine.com (www.hakaimagazine.com)... 23.185.0.4, 2620:12a:8001::4, 2620:12a:8000::4\n",
      "Connecting to www.hakaimagazine.com (www.hakaimagazine.com)|23.185.0.4|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 452718 (442K) [image/jpeg]\n",
      "Saving to: ‘./data/img1.JPG’\n",
      "\n",
      "./data/img1.JPG     100%[===================>] 442.11K  --.-KB/s    in 0.02s   \n",
      "\n",
      "2021-10-26 00:13:35 (28.3 MB/s) - ‘./data/img1.JPG’ saved [452718/452718]\n",
      "\n",
      "--2021-10-26 00:13:36--  https://www.artis.nl/media/filer_public_thumbnails/filer_public/00/f1/00f1b6db-fbed-4fef-9ab0-84e944ff11f8/chimpansee_amber_r_1920x1080.jpg__1920x1080_q85_subject_location-923%2C365_subsampling-2.jpg\n",
      "Resolving www.artis.nl (www.artis.nl)... 94.75.225.20\n",
      "Connecting to www.artis.nl (www.artis.nl)|94.75.225.20|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 361413 (353K) [image/jpeg]\n",
      "Saving to: ‘./data/img2.JPG’\n",
      "\n",
      "./data/img2.JPG     100%[===================>] 352.94K   790KB/s    in 0.4s    \n",
      "\n",
      "2021-10-26 00:13:38 (790 KB/s) - ‘./data/img2.JPG’ saved [361413/361413]\n",
      "\n",
      "--2021-10-26 00:13:38--  https://www.familyhandyman.com/wp-content/uploads/2018/09/How-to-Avoid-Snakes-Slithering-Up-Your-Toilet-shutterstock_780480850.jpg\n",
      "Resolving www.familyhandyman.com (www.familyhandyman.com)... 104.18.201.107, 104.18.202.107, 2606:4700::6812:ca6b, ...\n",
      "Connecting to www.familyhandyman.com (www.familyhandyman.com)|104.18.201.107|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 90994 (89K) [image/jpeg]\n",
      "Saving to: ‘./data/img3.JPG’\n",
      "\n",
      "./data/img3.JPG     100%[===================>]  88.86K  --.-KB/s    in 0.009s  \n",
      "\n",
      "2021-10-26 00:13:38 (9.64 MB/s) - ‘./data/img3.JPG’ saved [90994/90994]\n",
      "\n",
      "--2021-10-26 00:13:39--  https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json\n",
      "Resolving s3.amazonaws.com (s3.amazonaws.com)... 52.217.225.168\n",
      "Connecting to s3.amazonaws.com (s3.amazonaws.com)|52.217.225.168|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 35363 (35K) [application/octet-stream]\n",
      "Saving to: ‘./data/imagenet_class_index.json’\n",
      "\n",
      "./data/imagenet_cla 100%[===================>]  34.53K  --.-KB/s    in 0.07s   \n",
      "\n",
      "2021-10-26 00:13:39 (486 KB/s) - ‘./data/imagenet_class_index.json’ saved [35363/35363]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!mkdir ./data\n",
    "!wget  -O ./data/img0.JPG \"https://d17fnq9dkz9hgj.cloudfront.net/breed-uploads/2018/08/siberian-husky-detail.jpg?bust=1535566590&width=630\"\n",
    "!wget  -O ./data/img1.JPG \"https://www.hakaimagazine.com/wp-content/uploads/header-gulf-birds.jpg\"\n",
    "!wget  -O ./data/img2.JPG \"https://www.artis.nl/media/filer_public_thumbnails/filer_public/00/f1/00f1b6db-fbed-4fef-9ab0-84e944ff11f8/chimpansee_amber_r_1920x1080.jpg__1920x1080_q85_subject_location-923%2C365_subsampling-2.jpg\"\n",
    "!wget  -O ./data/img3.JPG \"https://www.familyhandyman.com/wp-content/uploads/2018/09/How-to-Avoid-Snakes-Slithering-Up-Your-Toilet-shutterstock_780480850.jpg\"\n",
    "\n",
    "!wget  -O ./data/imagenet_class_index.json \"https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from PIL import Image\n",
    "from torchvision import transforms\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, axes = plt.subplots(nrows=2, ncols=2)\n",
    "\n",
    "for i in range(4):\n",
    "    img_path = './data/img%d.JPG'%i\n",
    "    img = Image.open(img_path)\n",
    "    preprocess = transforms.Compose([\n",
    "        transforms.Resize(256),\n",
    "        transforms.CenterCrop(224),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n",
    "])\n",
    "    input_tensor = preprocess(img)      \n",
    "    plt.subplot(2,2,i+1)\n",
    "    plt.imshow(img)\n",
    "    plt.axis('off')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of classes in ImageNet: 1000\n"
     ]
    }
   ],
   "source": [
    "import json \n",
    "  \n",
    "with open(\"./data/imagenet_class_index.json\") as json_file: \n",
    "    d = json.load(json_file)\n",
    "    \n",
    "print(\"Number of classes in ImageNet: {}\".format(len(d)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def rn50_preprocess():\n",
    "    preprocess = transforms.Compose([\n",
    "        transforms.Resize(256),\n",
    "        transforms.CenterCrop(224),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n",
    "    ])\n",
    "    return preprocess\n",
    "\n",
    "# decode the results into ([predicted class, description], probability)\n",
    "def predict(img_path, model):\n",
    "    img = Image.open(img_path)\n",
    "    preprocess = rn50_preprocess()\n",
    "    input_tensor = preprocess(img)\n",
    "    input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model\n",
    "    \n",
    "    # move the input and model to GPU for speed if available\n",
    "    if torch.cuda.is_available():\n",
    "        input_batch = input_batch.to('cuda')\n",
    "        model.to('cuda')\n",
    "\n",
    "    with torch.no_grad():\n",
    "        output = model(input_batch)\n",
    "        # Tensor of shape 1000, with confidence scores over Imagenet's 1000 classes\n",
    "        sm_output = torch.nn.functional.softmax(output[0], dim=0)\n",
    "        \n",
    "    ind = torch.argmax(sm_output)\n",
    "    return d[str(ind.item())], sm_output[ind] #([predicted class, description], probability)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./data/img0.JPG - Predicted: ['n02110185', 'Siberian_husky'], Probablility: 0.49787256121635437\n",
      "./data/img1.JPG - Predicted: ['n01820546', 'lorikeet'], Probablility: 0.6447006464004517\n",
      "./data/img2.JPG - Predicted: ['n02481823', 'chimpanzee'], Probablility: 0.9899842739105225\n",
      "./data/img3.JPG - Predicted: ['n01749939', 'green_mamba'], Probablility: 0.4564124643802643\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(4):\n",
    "    img_path = './data/img%d.JPG'%i\n",
    "    img = Image.open(img_path)\n",
    "    \n",
    "    pred, prob = predict(img_path, resnet50_model)\n",
    "    print('{} - Predicted: {}, Probablility: {}'.format(img_path, pred, prob))\n",
    "\n",
    "    plt.subplot(2,2,i+1)\n",
    "    plt.imshow(img);\n",
    "    plt.axis('off');\n",
    "    plt.title(pred[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Benchmark utility"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us define a helper function to benchmark a model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import numpy as np\n",
    "\n",
    "import torch.backends.cudnn as cudnn\n",
    "cudnn.benchmark = True\n",
    "\n",
    "def benchmark(model, input_shape=(1024, 1, 224, 224), dtype='fp32', nwarmup=50, nruns=10000):\n",
    "    input_data = torch.randn(input_shape)\n",
    "    input_data = input_data.to(\"cuda\")\n",
    "    if dtype=='fp16':\n",
    "        input_data = input_data.half()\n",
    "        \n",
    "    print(\"Warm up ...\")\n",
    "    with torch.no_grad():\n",
    "        for _ in range(nwarmup):\n",
    "            features = model(input_data)\n",
    "    torch.cuda.synchronize()\n",
    "    print(\"Start timing ...\")\n",
    "    timings = []\n",
    "    with torch.no_grad():\n",
    "        for i in range(1, nruns+1):\n",
    "            start_time = time.time()\n",
    "            features = model(input_data)\n",
    "            torch.cuda.synchronize()\n",
    "            end_time = time.time()\n",
    "            timings.append(end_time - start_time)\n",
    "            if i%10==0:\n",
    "                print('Iteration %d/%d, ave batch time %.2f ms'%(i, nruns, np.mean(timings)*1000))\n",
    "\n",
    "    print(\"Input shape:\", input_data.size())\n",
    "    print(\"Output features size:\", features.size())\n",
    "    print('Average batch time: %.2f ms'%(np.mean(timings)*1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warm up ...\n",
      "Start timing ...\n",
      "Iteration 10/100, ave batch time 109.12 ms\n",
      "Iteration 20/100, ave batch time 109.08 ms\n",
      "Iteration 30/100, ave batch time 109.10 ms\n",
      "Iteration 40/100, ave batch time 109.12 ms\n",
      "Iteration 50/100, ave batch time 109.11 ms\n",
      "Iteration 60/100, ave batch time 109.10 ms\n",
      "Iteration 70/100, ave batch time 109.10 ms\n",
      "Iteration 80/100, ave batch time 109.11 ms\n",
      "Iteration 90/100, ave batch time 109.13 ms\n",
      "Iteration 100/100, ave batch time 109.13 ms\n",
      "Input shape: torch.Size([128, 3, 224, 224])\n",
      "Output features size: torch.Size([128, 1000])\n",
      "Average batch time: 109.13 ms\n"
     ]
    }
   ],
   "source": [
    "# Model benchmark without Torch-TensorRT\n",
    "model = resnet50_model.eval().to(\"cuda\")\n",
    "benchmark(model, input_shape=(128, 3, 224, 224), nruns=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"3\"></a>\n",
    "## 3. Creating TorchScript modules\n",
    "\n",
    "To compile with Torch-TensorRT, the model must first be in **TorchScript**. TorchScript is a programming language included in PyTorch which removes the Python dependency normal PyTorch models have. This conversion is done via a JIT compiler which given a PyTorch Module will generate an equivalent TorchScript Module. There are two paths that can be used to generate TorchScript: **Tracing** and **Scripting**. \n",
    "\n",
    "- Tracing follows execution of PyTorch generating ops in TorchScript corresponding to what it sees. \n",
    "- Scripting does an analysis of the Python code and generates TorchScript, this allows the resulting graph to include control flow which tracing cannot do. \n",
    "\n",
    "Tracing is more likely to compile successfully with Torch-TensorRT due to simplicity (though both systems are supported). We start with an example of the traced model in TorchScript."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tracing\n",
    "\n",
    "Tracing follows the path of execution when the module is called and records what happens. This recording is what the TorchScript IR will describe. \n",
    "\n",
    "To trace an instance of the model, we can call torch.jit.trace with an example input. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = resnet50_model.eval().to(\"cuda\")\n",
    "traced_model = torch.jit.trace(model, [torch.randn((128, 3, 224, 224)).to(\"cuda\")])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can save this model and use it independently of Python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is just an example, and not required for the purposes of this demo\n",
    "torch.jit.save(traced_model, \"resnet_50_traced.jit.pt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warm up ...\n",
      "Start timing ...\n",
      "Iteration 10/100, ave batch time 109.04 ms\n",
      "Iteration 20/100, ave batch time 109.04 ms\n",
      "Iteration 30/100, ave batch time 109.03 ms\n",
      "Iteration 40/100, ave batch time 109.05 ms\n",
      "Iteration 50/100, ave batch time 109.04 ms\n",
      "Iteration 60/100, ave batch time 109.04 ms\n",
      "Iteration 70/100, ave batch time 109.04 ms\n",
      "Iteration 80/100, ave batch time 109.04 ms\n",
      "Iteration 90/100, ave batch time 109.05 ms\n",
      "Iteration 100/100, ave batch time 109.06 ms\n",
      "Input shape: torch.Size([128, 3, 224, 224])\n",
      "Output features size: torch.Size([128, 1000])\n",
      "Average batch time: 109.06 ms\n"
     ]
    }
   ],
   "source": [
    "# Obtain the average time taken by a batch of input\n",
    "benchmark(traced_model, input_shape=(128, 3, 224, 224), nruns=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"4\"></a>\n",
    "## 4. Compiling with Torch-TensorRT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TorchScript modules behave just like normal PyTorch modules and are intercompatible. From TorchScript we can now compile a TensorRT based module. This module will still be implemented in TorchScript but all the computation will be done in TensorRT.\n",
    "\n",
    "As mentioned earlier, we start with an example of Torch-TensorRT compilation with the traced model.\n",
    "\n",
    "Note that we show benchmarking results of two precisions: FP32 (single precision) and FP16 (half precision)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### FP32 (single precision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: [Torch-TensorRT TorchScript Conversion Context] - The logger passed into createInferBuilder differs from one already provided for an existing builder, runtime, or refitter. TensorRT maintains only a single logger pointer at any given time, so the existing value, which can be retrieved with getLogger(), will be used instead. In order to use a new logger, first destroy all existing builder, runner or refitter objects.\n",
      "\n",
      "WARNING: [Torch-TensorRT] - Dilation not used in Max pooling converter\n",
      "WARNING: [Torch-TensorRT] - Detected invalid timing cache, setup a local cache instead\n"
     ]
    }
   ],
   "source": [
    "import torch_tensorrt\n",
    "\n",
    "# The compiled module will have precision as specified by \"op_precision\".\n",
    "# Here, it will have FP16 precision.\n",
    "trt_model_fp32 = torch_tensorrt.compile(traced_model, **{\n",
    "    \"inputs\": [torch_tensorrt.Input((128, 3, 224, 224), dtype=torch.float32)],\n",
    "    \"enabled_precisions\": {torch.float32}, # Run with FP32\n",
    "    \"workspace_size\": 1 << 22\n",
    "})\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warm up ...\n",
      "Start timing ...\n",
      "Iteration 10/100, ave batch time 93.58 ms\n",
      "Iteration 20/100, ave batch time 85.57 ms\n",
      "Iteration 30/100, ave batch time 92.02 ms\n",
      "Iteration 40/100, ave batch time 89.07 ms\n",
      "Iteration 50/100, ave batch time 86.80 ms\n",
      "Iteration 60/100, ave batch time 89.88 ms\n",
      "Iteration 70/100, ave batch time 88.58 ms\n",
      "Iteration 80/100, ave batch time 87.30 ms\n",
      "Iteration 90/100, ave batch time 86.28 ms\n",
      "Iteration 100/100, ave batch time 88.27 ms\n",
      "Input shape: torch.Size([128, 3, 224, 224])\n",
      "Output features size: torch.Size([128, 1000])\n",
      "Average batch time: 88.27 ms\n"
     ]
    }
   ],
   "source": [
    "# Obtain the average time taken by a batch of input\n",
    "benchmark(trt_model_fp32, input_shape=(128, 3, 224, 224), nruns=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### FP16 (half precision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: [Torch-TensorRT] - For input x.1, found user specified input dtype as Float16, however when inspecting the graph, the input type expected was inferred to be Float\n",
      "The compiler is going to use the user setting Float16\n",
      "This conflict may cause an error at runtime due to partial compilation being enabled and therefore\n",
      "compatibility with PyTorch's data type convention is required.\n",
      "If you do indeed see errors at runtime either:\n",
      "- Remove the dtype spec for x.1\n",
      "- Disable partial compilation by setting require_full_compilation to True\n",
      "WARNING: [Torch-TensorRT TorchScript Conversion Context] - The logger passed into createInferBuilder differs from one already provided for an existing builder, runtime, or refitter. TensorRT maintains only a single logger pointer at any given time, so the existing value, which can be retrieved with getLogger(), will be used instead. In order to use a new logger, first destroy all existing builder, runner or refitter objects.\n",
      "\n",
      "WARNING: [Torch-TensorRT] - Dilation not used in Max pooling converter\n",
      "WARNING: [Torch-TensorRT] - Detected invalid timing cache, setup a local cache instead\n"
     ]
    }
   ],
   "source": [
    "import torch_tensorrt\n",
    "\n",
    "# The compiled module will have precision as specified by \"op_precision\".\n",
    "# Here, it will have FP16 precision.\n",
    "trt_model = torch_tensorrt.compile(traced_model, **{\n",
    "    \"inputs\": [torch_tensorrt.Input((128, 3, 224, 224), dtype=torch.half)],\n",
    "    \"enabled_precisions\": {torch.float, torch.half}, # Run with FP16\n",
    "    \"workspace_size\": 1 << 22\n",
    "})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warm up ...\n",
      "Start timing ...\n",
      "Iteration 10/100, ave batch time 18.76 ms\n",
      "Iteration 20/100, ave batch time 18.85 ms\n",
      "Iteration 30/100, ave batch time 18.93 ms\n",
      "Iteration 40/100, ave batch time 18.96 ms\n",
      "Iteration 50/100, ave batch time 18.92 ms\n",
      "Iteration 60/100, ave batch time 18.94 ms\n",
      "Iteration 70/100, ave batch time 18.98 ms\n",
      "Iteration 80/100, ave batch time 18.97 ms\n",
      "Iteration 90/100, ave batch time 19.08 ms\n",
      "Iteration 100/100, ave batch time 22.90 ms\n",
      "Input shape: torch.Size([128, 3, 224, 224])\n",
      "Output features size: torch.Size([128, 1000])\n",
      "Average batch time: 22.90 ms\n"
     ]
    }
   ],
   "source": [
    "# Obtain the average time taken by a batch of input\n",
    "benchmark(trt_model, input_shape=(128, 3, 224, 224), dtype='fp16', nruns=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"5\"></a>\n",
    "## 5. Conclusion\n",
    "\n",
    "In this notebook, we have walked through the complete process of compiling TorchScript models with Torch-TensorRT for ResNet-50 model and test the performance impact of the optimization. With Torch-TensorRT, we observe a speedup of **1.4X** with FP32, and **3.0X** with FP16.\n",
    "\n",
    "### What's next\n",
    "Now it's time to try Torch-TensorRT on your own model. Fill out issues at https://github.com/NVIDIA/Torch-TensorRT. Your involvement will help future development of Torch-TensorRT.\n"
   ]
  }
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